Analysis of the causes of a sharp increase in the incidence of chronic hepatitis C in the Omsk Region using Bayesian analysis of hepatitis C virus nucleotide sequences

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Introduction

Parenteral viral hepatitis is a disease that causes significant socioeconomic damage and is characterized by widespread prevalence, a tendency to become chronic, the predominance of subclinical forms of infection, and a high frequency of cirrhosis and hepatocellular carcinoma[1], which determines the social significance of this group of infections¹. Currently, a program is being implemented worldwide, including in Russia, to eliminate viral hepatitis as a threat to public health by 2030². This program aims to significantly reduce the incidence and mortality of viral hepatitis by increasing the coverage of anti-epidemic measures (diagnosis, vaccination, blood donor safety) and antiviral therapy [2]. In the case of hepatitis C, the most important anti-epidemic measures are diagnosis, i.e., the identification of infected individuals, and therapy, i.e., reducing the number of sources of infection in the population. The diagnosis of hepatitis C consists of two stages. The first stage is screening for antibodies to the hepatitis C virus (anti-HCV), and the coverage of this type of testing is an important indicator of the coverage of the population with hepatitis C diagnosis. The second stage is determining the presence of HCV itself in the body by confirming viremia, more often by determining HCV RNA in plasma.

The implementation of anti-epidemic measures requires systematic genetic monitoring of hepatitis B, C, and D viruses, which allows tracking changes in the pathogen population, including their adaptation to external influences. In addition to understanding the fundamental patterns of hepatitis virus circulation, genetic monitoring allows us to answer practical questions related to the surveillance of these infections: deciphering outbreaks, assessing the intensity of pathogen transmission, tracking the emergence of new genetic variants, including those with adverse properties (drug resistance, evasion of the immune response).

Overall, genomic surveillance of HCV is an important tool that not only provides insight into the diversity of genetic variants of the virus currently circulating within the country or imported from abroad. It also allows, when using adequate analysis methods, to reconstruct the history of transmission of different genetic lineages of the pathogen in a given territory and, based on these data, to answer questions arising during hepatitis C surveillance [3–7]. An example is the analysis of the causes of the sharp increase in the incidence of chronic hepatitis C in the Omsk region in 2022–2024, the highest among the constituent entities of the Russian Federation, using genomic surveillance tools.

In recent years, there has been a sharp increase in the incidence of chronic hepatitis C in the Omsk region. Until 2021, the incidence rate of chronic hepatitis C in the region varied between 14.8 and 29.3 per 100,000. Starting in 2022, the rate rose, reaching 39.3⁰/₀₀₀₀ in 2022, 107.7⁰/₀₀₀₀ in 2023, and 125.5⁰/₀₀₀₀ in 2024. In 2022, 747 patients with chronic hepatitis C were identified in the Omsk region, in 2023 — 1,983, and in 2024 — 2,290. Starting in 2019 and continuing annually, the incidence of chronic hepatitis C in the Omsk region exceeded the Russian average; in 2023 and 2024, the excess was 3.4 and 3.6 times, respectively³. In connection with this unfavorable situation, the following question arose: is the observed increase in incidence a consequence of a recent increase in the number of infections, i.e., the intensification of the transmission of the pathogen, or does it reflect an increase in the coverage with diagnosis of people who have been infected for a long time? This question is not specific to a particular region, since the increase in the coverage of the population with screening for hepatitis C markers as part of the program to combat this infection suggests that the registered incidence of chronic hepatitis C in the country will increase in the coming years. There is no data in the literature on the increase in chronic hepatitis C cases in the Omsk region and the reasons for this phenomenon, which determined the necessity for our study.

The aim of the study was to answer the question of whether the observed increase in the incidence of chronic hepatitis C in the Omsk region is a consequence of the recent sharp increase in HCV infections. To this end, data on hepatitis C marker testing coverage of contingents in accordance with Appendices 17 and 18 of SanPiN 3.3686-21 “Sanitary and epidemiological requirements for the prevention of infectious diseases” in the Omsk region in 2014–2024 were analyzed. Bayesian phylogenetic analysis was used to determine the presence of genetic links between HCV variants isolated from patients with chronic hepatitis C diagnosed in 2022–2023.

Materials and methods

Samples

For molecular genetic research, plasma and blood serum samples from 596 patients with newly diagnosed chronic hepatitis C were transferred from the Federal Budgetary Institution “Center for Hygiene and Epidemiology in the Omsk Region” to the Reference Center for Monitoring Viral Hepatitis at the Central Research Institute of Epidemiology: 333 patients were identified in 2023, 183 patients in 2022, and 80 patients in 2017–2021 and earlier.

The sample included 332 (55.7%) men aged 2–85 years, with a median age of 48 years (0–14 years — 1 patient; 15–19 years — 1; 20–29 years — 11; 30–39 years — 66; 40–49 years old — 106; 50–59 years old — 68; 60 years old and older — 79); 264 (44.3%) women aged 2–88 years, median age — 51 years (0–14 years old — 1 patient; 15–19 years old — 1; 20–29 years old — 14; 30–39 years old — 43; 40–49 years old — 64; 50–59 years old — 62; 60 years old and older — 79).

Samples were collected as part of an investigation in accordance with the order of the head of Rospotrebnadzor dated October 26, 2023, No. 02/18323-26. Biomaterial was collected as part of routine testing for hepatitis C markers. Patients signed informed consent forms for medical intervention. Ethical approval was not required, as only residual blood plasma and serum samples obtained during routine laboratory tests were provided. All of the provided biosamples were completely anonymized, with no identifying information about the patients, making it impossible to establish their identities. At the Center for Hygiene and Epidemiology in the Omsk Region, blood samples were used to prepare plasma and serum samples using standard methods, which were then transported in frozen form, maintaining the cold chain, to the Reference Center for Monitoring Viral Hepatitis at the Central Research Institute of Epidemiology. Prior to testing, the samples were stored in 1 ml aliquots in frozen form at -70°C.

Analysis of morbidity rates and testing coverage

An analysis of the dynamics of acute hepatitis C and chronic hepatitis C incidence rates in Russia and the Omsk Region was conducted using Federal Statistical Observation Form No. 2, “Information on Infectious and Parasitic Diseases,” for 1994–2024. The coverage of anti-HCV testing in Russia and the Omsk region, the frequency of anti-HCV detection among the contingents that were subject to examination, as well as the proportion of contingents among persons with anti-HCV in the Omsk region were determined based on data from the Automated Information System “Viral Hepatitis” Reference Center for Monitoring Viral Hepatitis of the Central Research Institute of Epidemiology for 2011–2024.

Molecular biological research

RNA was extracted from 1000 μl blood plasma samples using the Magno-Sorb reagent kit (Central Research Institute of Epidemiology). Qualitative determination of HCV RNA by PCR combined with reverse transcription (RT-PCR) was performed using the Amplisens HCV/HBV/HIV-FL reagent kit (Central Research Institute of Epidemiology) reagent kit, with an HCV RNA detection limit of 10 IU/ml. For quantitative determination of HCV RNA, the Amplisens HCV-Monitor-FL reagent kit (Central Research Institute of Epidemiology) was used.

Sequencing and phylogenetic analysis

For all HCV RNA-positive samples, a 942-nucleotide fragment of the viral genome encoding Core and E1 proteins (nucleotide positions 293–1234 according to the reference sequence of HCV 1a strain H77, GenBank #AF011753) was amplified. Reverse transcription was performed with random hexamer primers using the MMLV RT reagent kit (Eurogen). The resulting cDNA was amplified by PCR using primers to the HCV Core/E1 genome region in accordance with a previously described protocol [11]. The amplification product was cut from the gel and isolated from agarose using the QIAquick Gel Extraction kit (Qiagen). Sequencing was performed on a 3500 Genetic Analyzer (Thermo Fisher Scientific) using the Big Dye Terminator v 3.1 Cycle Sequencing Kit (Thermo Fisher Scientific).

To determine the HCV genotype and establish the genetic relationship between the identified virus gene variants, time-scaled phylogenetic analysis was performed for the obtained sequences of the HCV genome fragment encoding the Core and E1 proteins using the Bayesian approach implemented in the BEAST2 software package. Reference sequences were used to determine the genotype, in accordance with the ICTV recommendations dated March 9, 2022. To determine the relationship between the analyzed samples, all available archival sequences isolated in Russia were added to the sample, as well as 10 sequences per sample from the international GenBank database that showed the highest degree of identity. To the 344 sequences from Omsk region obtained in this study, 219 HCV sequences obtained from different regions of the Russian Federation in different years were added to the analysis, as well as 395 sequences from GenBank obtained as a result of searching for sequences isolated in the Omsk region in BLASTn against the database (nr) with coverage of at least 98% and E-value < 0.05 and selected after removal of those with a degree of difference of less than 5% using the Skipredundant Jemboss algorithm. For all 958 sequences included in the analysis, the year and country of collection were known, and in the case of Russia, the city/region. The parameters selected based on test runs, in which various combinations of molecular clock and population models were tested, were as follows: BEAST Model Test, relaxed log-normal clock with a starting substitution rate of 5.58 × 10^–4 substitutions/site/year, determined during test runs, population model—merging constant population. The length of iterations of the Markov chain Monte Carlo algorithm was 50 million. After completion of the calculation, the rate of accumulation of substitutions was 1.78 × 10^–3 substitutions/site/year.

The reliability of the results obtained was assessed using the Tracer program; the ESS of all parameters was greater than 200. For the analyzed sample of sequences, a linear regression curve was obtained (Fig. 1), indicating a positive correlation between genetic divergence and time, estimated using TempEst. The tree was visualized using the FigTree program. A phylogenetic tree common to all HCV genotypes was constructed using the maximum likelihood algorithm in IQ-Tree, then for illustrative purposes, certain genotypes were left visible at the visualization stage, and the rest were compressed.

 

Fig. 1. Linear regression graph of the temporal signal for HCV sequences encoding Core and E1 proteins (942 bp, positions 293–1234, numbered according to the genome of the prototype strain H77, GenBank #AF011753).

 

Results

Analysis of HCV incidence and testing coverage among populations subject to screening for hepatitis C markers

The dynamics of acute and chronic hepatitis C incidence in the Omsk region compared to the average annual indicators (AAI) are shown in Fig. 2.

 

Fig. 2. Incidence of acute and chronic hepatitis C in the Omsk Region and on average in Russia in 1994–2024.

 

According to data from Form No. 2 of the Federal Statistical Survey, “Information on Infectious and Parasitic Diseases,” a total of 154 cases of acute hepatitis C and 6,984 cases of chronic hepatitis C were detected in the Omsk Region between 2011 and 2021. The incidence rate of acute hepatitis C during this period did not exceed 1⁰/₀₀₀₀. Until 2022, the incidence rate of chronic hepatitis C varied between 14.8 and 29.3⁰/₀₀₀₀, not exceeding the AAI. Since 2022, there has been an increase in the incidence rate of chronic hepatitis C, which was 39.5⁰/₀₀₀₀ in 2022, 107.67⁰/₀₀₀₀ in 2023, and 147.17⁰/₀₀₀₀ in 2024, exceeding the AAI by 1.2. — 107.67⁰/₀₀₀₀, and in 2024 — 147.17⁰/₀₀₀₀, exceeding the AAI by 1.2, 3.2, and 3.7 times in 2022, 2023, and 2024, respectively.

Results of the analysis of anti-HCV testing coverage among the contingents subject to examination in accordance with Appendices 17 and 18 of SanPiN 3.3686-21 “Sanitary and epidemiological requirements for the prevention of infectious diseases” in the Omsk region in 2011–2024, and the frequency of anti-HCV detection among those examined are shown in Fig. 3 and Fig. 4. These data reflect an increase in testing coverage in 2023–2024, as well as a trend toward an increase in the proportion of reactive tests from 2018 to 2024, which may be explained by more targeted testing and the inclusion of a significant number of individuals with HCV infection in the surveyed groups.

 

Fig. 3. Incidence of chronic hepatitis C and coverage of anti-HCV testing among populations subject to screening in the Omsk region in 2011–2024

 

Fig. 4. Frequency of anti-HCV detection among the contingents that were subject to examination and the proportion of contingents among persons with anti-HCV in the Omsk region in 2011–2024.

1–17 — groups subject to anti-HCV testing: 1 — donors (total); 2 — pregnant women; 3 — recipients of blood and blood components; 4 — newborns of women with hepatitis B and/or hepatitis C; 5 — blood service personnel; 6 — staff of hemodialysis, kidney transplant, cardiovascular and pulmonary surgery, and hematology departments; 7 — staff of clinical diagnostic and biochemical laboratories; 8 — staff of surgical, urological, obstetric-gynecological, anesthesiological, resuscitation, dental, infectious, and gastroenterological hospitals, departments, and clinics, as well as staff of emergency stations and departments; 9 — patients of hemodialysis, kidney transplant, cardiovascular and pulmonary surgery, and hematology centers and departments; 10 — patients with chronic liver damage; 11 — patients with chronic pathologies (tuberculosis, oncological diseases, psychoneurological diseases, etc.), except for chronic liver pathologies; 12 — patients of narcological and dermatological-venereological dispensaries, offices, and hospitals; 13 — patients admitted to hospitals for planned surgical interventions; 14 — wards and staff of closed children's institutions (children's homes, orphanages, special boarding schools, etc.); 15 — contacts in hepatitis B and hepatitis C outbreaks; 16 — contingents of federal penal service institutions; 17 — others.

 

Among individuals who tested positive for anti-HCV during routine screening in accordance with SanPiN 3.3686-21, the largest contribution in recent years has been made by patients with chronic liver damage, patients with chronic pathologies (tuberculosis, oncological diseases, psychoneurological diseases, etc.), patients of narcological and dermatological-venereological dispensaries, clinics, hospitals, patients admitted to hospitals for routine surgical procedures, and others (persons not included in the surveyed groups but tested for anti-HCV for medical reasons — groups 10–13 in Fig. 4).

Results of molecular biological studies

A total of 596 blood plasma samples from patients with chronic hepatitis C, selected by random sampling in 2024, were analyzed, accounting for 12.5% of all newly diagnosed cases of chronic hepatitis C in the Omsk region during this period (n = 2730).

HCV RNA was detected in 386 samples. Thus, the frequency of HCV RNA detection, indicating the presence of chronic hepatitis C, was 64.8% in the studied cohort. The absence of HCV RNA in 35.2% of samples from patients diagnosed with chronic hepatitis C indicates a high level of overdiagnosis of hepatitis C, since a diagnosis of chronic hepatitis C can only be made if the presence of HCV RNA in blood plasma is confirmed⁴.

Bayesian phylogenetic analysis of HCV sequences from the Omsk region isolated in 2024

Sequences of the Core/E1 region of the HCV genome were obtained for 344 (89.1%) of 386 HCV RNA-positive samples, including 201 samples from patients with newly diagnosed chronic hepatitis C in 2023 and 143 samples from patients with newly diagnosed chronic hepatitis C in 2024. In 42 samples for which the Core/E1 fragment could not be amplified, the HCV RNA concentration was less than 3 log10 IU/mL.

Phylogenetic analysis with a time scale was performed on the HCV sequences obtained in order to calculate the time to most recent common ancestor (tMRCA) for sequences from patients in the Omsk region. Calculation of this parameter allows us to assess whether the pathogen was transmitted as a result of mass infection over a short period of time, for example, as a result of an outbreak, or whether the detected cases of HCV infection are the result of sequential infection and reflect the long-term circulation of the pathogen in the region. The overall phylogenetic tree obtained for all HCV genotypes with a graphical representation of the 95% highest posterior density (95% HPD) interval is shown in Fig. 5. Further in the text of the article, the phylogenetic trees are given with the tMRCA value expressed in calendar years, but without a graphical representation of the 95% HPD to facilitate visual perception of the figures.

 

Fig. 5. Bayesian phylogenetic tree for HCV sequences encoding Core and E1 proteins (942 bp, positions 293–1234, numbered according to the genome of the prototype strain H77, GenBank #AF011753).

The region and year of isolation are indicated for each sequence. Sequences from the Omsk region are highlighted in red. Tree branches with a posterior probability > 90% are highlighted in red. In each node, 95% HPD is displayed as a blue band.

 

Phylogenetic analysis revealed 21 HCV sequences of genotype 1a, 145 of genotype 1b, 11 of genotype 2a, 1 of genotype 2c, 5 of genotype 2k, 160 of genotype 3a, and 1 of genotype 4a. Among the HCV genotypes found in the patients from the Omsk region, genotypes 3a (46.5%) and 1b (42.2%) were the most common, with genotype 1a accounting for 6.1% and genotype 2 (subgenotypes 2a, 2c, and 2k) accounting for 4.9%.

The results of time-scaled phylogenetic analysis for HCV genotype 1a are presented in Fig. 6.

 

Fig. 6. Phylogenetic tree with a time scale for HCV sequences of genotype 1a encoding Core and E1 proteins (942 bp, positions 293–1234, numbered according to the genome of the prototype strain H77, GenBank #AF011753).

The region and year of isolation are indicated for each sequence. Sequences from the Omsk region are highlighted in red. Tree branches with a posterior probability > 90% are highlighted in red. The scale reflects time in calendar years. The tree nodes indicate tMRCA expressed in calendar years.

 

Analysis of 21 HCV genotype 1a sequences isolated from patients identified in 2023–2024 in the Omsk region demonstrated the presence of two regional clusters that formed in the 2000s (95% HPD: 1997–2009). An estimated tMRCA no later than 2020 was established for 19 (90%) sequences in the identified HCV genotype 1a clusters, suggesting no recent epidemiological link between these cases of infection. At the same time, 2 (10%) sequences form a pair with a divergence in 2021 (95% HPD 2018–2023) (sequences 3602 and 4670 in Fig. 6).

The results of time-scaled phylogenetic analysis for HCV genotype 1b are presented in Fig. 7. Of the 145 HCV genotype 1b sequences from patients in the Omsk region, 126 were grouped into 9 clusters (marked with numbers 1, 2, 4–9 in Fig. 7), consisting solely or predominantly of sequences from the Omsk region, including 4 to 28 isolates, and formed in 1990–2000 (95% HPD 1980–2014). This grouping indicates the sequential transmission of HCV of this genotype in the Omsk region, leading to the formation of regional clusters. The remaining HCV-1b sequences (n = 63) did not belong to regional clusters and were represented by single isolates or pairs of isolates grouped with sequences from other regions of the Russian Federation. The tMRCA value for 126 (86.9%) sequences in the identified clusters was at least 10 years (i.e., no later than 2014), indicating no recent epidemiological link between these cases of infection. At the same time, 4 (2.8%) sequences form 2 pairs with a divergence no earlier than 2022 (95% HPD 2022–2024), suggesting recent infection and an epidemiological link in these pairs of infection cases: sequences 3720 and 3722 in cluster 1 and sequences 3682 and 3701 in cluster 3 (Fig. 7).

 

Fig. 7. Phylogenetic tree with a timeline for HCV sequences of genotype 1b encoding Core and E1 proteins (942 bp, positions 293–1234, numbered according to the genome of the prototype strain H77, GenBank #AF011753).

The region and year of isolation are indicated for each sequence. Sequences from the Omsk region are highlighted in red. Tree branches with a posterior probability > 90% are highlighted in red. The scale reflects time in calendar years. The tree nodes indicate tMRCA expressed in calendar years.

 

The results of time-scaled phylogenetic analysis for HCV genotype 2 (subtypes 2a, 2c, and 2k) are presented in Fig. 8. Among the 17 HCV genotype 2 sequences from the Omsk region, 15 (88%) did not form clusters or pairs, but two subtype 2a sequences (sequences 3673 and 3681) formed a pair with a tMRCA value corresponding to 2023 (95% HPD 2022–2024), indicating recent infection and suggesting an epidemiological link between the two cases of infection (Fig. 8).

 

Fig. 8. Phylogenetic tree with a time scale for HCV sequences of genotype 2 encoding Core and E1 proteins (942 bp, positions 293–1234, numbered according to the genome of the prototype strain H77, GenBank #AF011753).

The region and year of isolation are indicated for each sequence. Sequences from the Omsk region are highlighted in red. Tree branches with a posterior probability > 90% are highlighted in red. The scale reflects time in calendar years. The tree nodes indicate tMRCA expressed in calendar years.

 

The results of time-scaled phylogenetic analysis for HCV genotype 3a are presented in Fig. 9. Of the 160 HCV genotype 3a sequences from patients in the Omsk region, 99 were grouped into six clusters (marked with numbers 1–6 in Fig. 9), consisting solely or predominantly of sequences from the Omsk region. These regional clusters contain 4 to 48 HCV genotype 3a isolates from the Omsk region and, similar to the genotype 1b sequence clusters, were formed in 1990–2000 (95% HPD 1987–2011). The remaining 61 HCV genotype 3a sequences from the Omsk region were not included in regional clusters and were represented by single isolates or pairs of isolates grouped with sequences from other regions of Russia. The tMRCA values for 147 (91.9%) genotype 3a sequences within clusters from the Omsk region were 10 years or more, suggesting no recent epidemiological link between these cases of infection. At the same time, 4 (2.5%) sequences formed 2 pairs with a divergence in 2023 (95% HPD 2022–2024), indicating recent infection and suggesting an epidemiological link between these cases of infection: samples 3878 and 4677 in cluster 2 and samples 3757 and 3860 in cluster 5 (Fig. 9).

 

Fig. 9. Phylogenetic tree with a time scale for HCV sequences of genotype 3a encoding Core and E1 proteins (942 bp, positions 293–1234, numbered according to the genome of the prototype strain H77, GenBank #AF011753).

The region and year of isolation are indicated for each sequence. Sequences from the Omsk region are highlighted in red. Tree branches with a posterior probability > 90% are highlighted in red. The scale reflects time in calendar years. The tree nodes indicate tMRCA expressed in calendar years.

 

Thus, time-scaled phylogenetic analysis of HCV sequences isolated from patients identified in 2023–2024 living in the Omsk region demonstrated the presence of regional clusters formed in 1990–2000 (95% HPD 1980–2014). The majority (86.0%, 295 out of 343) of HCV genotype 1a, 1b, 2a, 2c, 2k, and 3a sequences isolated in 2023–2024 belong to different lineages that have been circulating in the region for at least 10 years, and only a few pairs show signs of very recent transmission. A total of 12 (3.5%) sequences form pairs with a divergence in 2021–2023, indicating recent infection and suggesting an epidemiological link in these cases. Such pairs have been identified for HCV genotypes 1a, 1b, 2a, and 3a.

Discussion

The aim of this study was to analyze the reasons for the sharp increase in the incidence of chronic hepatitis C observed recently in the Omsk region. There are two possible explanations for this phenomenon. The first is a sharp increase in the transmission of the pathogen (e.g., a large outbreak), resulting in a significant number of recent cases of infection. The second is an increase in the coverage of the population with HCV screening, which has led to the detection of a significant number of people infected in the more distant past. To answer this question, we conducted both an analysis of the dynamics of chronic hepatitis C diagnosis coverage among different population groups in the region and a Bayesian analysis of HCV nucleotide sequences isolated from recently diagnosed cases of the disease. The results of the analysis of anti-HCV testing coverage among the contingents subject to examination in accordance with SanPiN 3.3686-21⁵ and the frequency of anti-HCV detection among those examined reflect an increase in testing coverage in 2023–2024, as well as an increase in the proportion of reactive tests, which may be explained by more targeted testing and the inclusion of a significant number of individuals with HCV infection in the examined groups — patients with chronic liver damage and/or belonging to risk groups.

In this case, analyzing data on the coverage of the population by diagnostics alone, without the use of molecular epidemiology methods, does not provide a clear answer, since an increase in the coverage of infection diagnostics can lead to an increase in the registered incidence of the disease, both in the case of a large number of long-standing cases of infection in the population and in the case of the recent spread of the virus. At the same time, Bayesian analysis of detected HCV genetic variants allows us to reliably reconstruct the history of HCV transmission in a given territory and determine how long ago the detected genetic variants of the virus separated from their common ancestor [9]. This approach has been used repeatedly to reconstruct the history of HCV genotype spread in specific areas [7, 10–13]. We have previously conducted similar studies to reconstruct the history of the introduction and spread of hepatitis B virus and hepatitis D virus genotypes [14, 15].

One of the most important methodological issues in conducting such studies is the representativeness of the sequence sample, i.e., the degree of population coverage by sequencing. Studies on the dynamics of HIV-1 transmission conducted using bioinformatics methods have shown that the coverage level required for the reliability of cluster analysis is > 10%, i.e., viral sequences isolated from more than 10% of infected individuals in the studied group must be analyzed [16]. In this study, samples from 19.6% of all newly diagnosed cases of chronic hepatitis C in 2022–2023 were included in the primary analysis. However, the frequency of HCV RNA detection, indicating the presence of chronic hepatitis C, was only 64.8% in the cohort studied, which suggests a significant level of overdiagnosis, since the diagnosis of chronic hepatitis C can only be made when HCV viremia is confirmed. It is evident that some patients were diagnosed based solely on the detection of anti-HCV. Thus, the actual number of patients with chronic hepatitis C among those identified in 2022–2023 in the Omsk region may be significantly lower than the official statistics (747 patients in 2022 and 1,983 patients in 2023). For this reason, it is not possible to accurately determine the level of sequencing coverage of the analyzed population (i.e., patients with chronic hepatitis C identified in 2022–2023 in the Omsk region). Nevertheless, we obtained HCV sequences from 12.6% of all new chronic hepatitis C cases in the region in 2022–2023, or from 19.5% of patients with suspected viremia (i.e., true chronic hepatitis C), taking into account the detection of HCV RNA in the examined cohort of chronic hepatitis C patients, which was 64.8%.

The second methodological issue is the selection of the region of the viral genome for analysis. In a number of the above-mentioned studies, NS5A or NS5B regions of varying lengths were analyzed. In our opinion, the use of a fragment of the HCV genome encoding the core and E1 proteins is preferable for Bayesian analysis, since it is long (942 nucleotides), is stably amplified with universal primers, and contains the E1 region, which has a high level of variability [17], which allows us to obtain a reliable association between genetic variability and time when performing Bayesian analysis.

The results of time-scaled phylogenetic analysis of HCV sequences from patients with chronic hepatitis C identified in the Omsk region in 2023, against the backdrop of peak levels of recorded incidence, proved that the current rise in chronic hepatitis C incidence in the Omsk region reflects the cumulative number of cases of chronic HCV infection in the region, which arose as a result of infection mainly 10–20 or more years ago. Of course, the estimated time to the most recent common ancestor for HCV sequences included in regional clusters is not the time of infection of the patients from whom the sequences were isolated. These patients may have been infected later than the dates indicated in the phylogenetic tree nodes, as the identified regional clusters may contain additional HCV sequences that have not yet been identified. Nevertheless, the studied sample of HCV sequences does not contain large, genetically homogeneous clusters with recent tMRCA values that would be characteristic of recent multiple transmission of the pathogen. The vast majority of HCV infection cases identified in the region in recent years belong to different, long-circulating lineages, which does not suggest the presence of a large outbreak that led to a sharp increase in the incidence of chronic hepatitis C in the Omsk region.

Overall, the results of the study using the developed methodology based on Bayesian analysis of the core/E1 region of the HCV genome allowed for an accurate assessment of the proportion of “old” and “new” HCV infections in the region. Given the increase in anti-HCV testing coverage under the National Hepatitis C Control Program and the involvement of groups with a significant number of undetected cases of infection in testing, a significant increase in the incidence of chronic hepatitis C can be expected in other regions of the country and in Russia as a whole. It is expected that with the increase in the incidence of chronic hepatitis C in other regions, a similar question will arise, the answer to which can be obtained using the proposed methodology. At the same time, the identification of six pairs of HCV sequences with an estimated divergence in 2021–2023 indicates recent infection of patients from whom the sequence data were obtained and suggests an epidemiological link between cases of infection, which requires an in-depth epidemiological analysis to rule out cases of healthcare-associated HCV infection.

Conclusion

The current increase in HCV incidence in the Omsk region appears to reflect the cumulative number of chronic HCV infections in the region that arose as a result of infection 10–20 or more years ago and is a consequence of increased diagnostic coverage of the population. With increased testing coverage and the involvement of groups with a significant number of undetected cases of infection, an increase in the incidence of chronic hepatitis C can also be expected in other regions of the country. At the same time, it is important that the territorial bodies of Rospotrebnadzor remain vigilant and analyze each registered case of chronic hepatitis C in order to detect healthcare-associated HCV infections.

 

¹ Resolution of the Government of the Russian Federation of 01.12.2004 No. 715 "List of socially significant diseases".

² Order of the Government of the Russian Federation dated 02.11.2022 No. 3306-r "On approval of the action plan to combat chronic viral hepatitis C in the Russian Federation until 2030."

³ State report "On the state of sanitary and epidemiological wellbeing of the population in the Russian Federation in 2023". Moscow; 2024.

⁴ Resolution of the Chief State Sanitary Doctor of the Russian Federation dated January 28, 2021 No. 4 "On approval of sanitary rules and regulations SanPiN 3.3686–21 "Sanitary and Epidemiological Requirements for the Prevention of Infectious Diseases".

⁵ Resolution of the Chief State Sanitary Doctor of the Russian Federation dated January 28, 2021 No. 4 "On approval of sanitary rules and regulations SanPiN 3.3686-21 "Sanitary and Epidemiological Requirements for the Prevention of Infectious Diseases".

About the authors

Vitalina V. Klushkina

Central Research Institute of Epidemiology

Author for correspondence.
Email: vitalinaklu@yandex.ru
ORCID iD: 0000-0001-8311-8204

Cand. Sci. (Med.), Head, Laboratory of viral hepatitis

Russian Federation, Moscow

Anastasia A. Karlsen

Central Research Institute of Epidemiology; Mechnikov Research Institute of Vaccines and Sera

Email: karlsen12@gmail.com
ORCID iD: 0000-0002-6013-7768

researcher, Laboratory of molecular epidemiology of viral hepatitis, Central Research Institute of Epidemiology; research fellow, Laboratory of viral hepatitis, I. Mechnikov Research Institute of Vaccines and Sera

Russian Federation, Moscow; Moscow

Vera S. Kichatova

Central Research Institute of Epidemiology; Mechnikov Research Institute of Vaccines and Sera

Email: kichatova@mail.ru
ORCID iD: 0000-0002-7838-6965

Cand. Sci. (Med.), senior researcher, Laboratory of molecular epidemiology of viral hepatitis, Central Research Institute of Epidemiology; senior researcher, Laboratory of viral hepatitis, I. Mechnikov Research Institute of Vaccines and Sera

Russian Federation, Moscow; Moscow

Fedor A. Asadi Mobarhan

Central Research Institute of Epidemiology; Mechnikov Research Institute of Vaccines and Sera

Email: 1amfa@bk.ru
ORCID iD: 0000-0002-1838-8037

researcher, Laboratory of molecular epidemiology of viral hepatitis, Central Research Institute of Epidemiology; researcher, Laboratory of viral hepatitis, I. Mechnikov Research Institute of Vaccines and Sera

Russian Federation, Moscow; Moscow

Olga V. Isaeva

Central Research Institute of Epidemiology; Mechnikov Research Institute of Vaccines and Sera; Russian Medical Academy of Continuing Professional Education

Email: isaeva.o@cmd.su
ORCID iD: 0000-0002-2656-3667

Dr. Sci. (Biol.), leading researcher, Laboratory of molecular epidemiology of viral hepatitis, Central Research Institute of Epidemiology; leading researcher, Laboratory of viral hepatitis, I. Mechnikov Research Institute of Vaccines and Sera; Associate Professor, Department of virology, Russian Medical Academy of Continuous Professional Educatio

Russian Federation, Moscow; Moscow; Moscow

Zinaida S. Rodionova

Central Research Institute of Epidemiology

Email: rodionova.z@cmd.su
ORCID iD: 0000-0003-0401-279X

researcher, Laboratory of viral hepatitis

Russian Federation, Moscow

Marina I. Korabelnikova

Central Research Institute of Epidemiology

Email: korabelnikova@cmd.su
ORCID iD: 0000-0002-2575-8569

researcher, Laboratory of viral hepatitis

Russian Federation, Moscow

Elena N. Kudravtseva

Central Research Institute of Epidemiology

Email: kudryavtseva@cmd.su
ORCID iD: 0000-0002-7325-8577

Dr. Sci. (Biol.), leading researcher, Laboratory of viral hepatitis

Russian Federation, Moscow

Lyudmila S. Gavrilova

Central Research Institute of Epidemiology

Email: gavrilova.l@cmd.su
ORCID iD: 0009-0003-5839-7055

researcher, Laboratory of viral hepatitis

Russian Federation, Moscow

Polina А. Pavlova

Central Research Institute of Epidemiology

Email: arhiv0709@gmail.com
ORCID iD: 0009-0008-3035-2409

laboratory assistant, Laboratory of viral hepatitis

Russian Federation, Moscow

Aleksandr А. Nikitin

Russian Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing office in Omsk Region

Email: rpn@55.rospotrebnadzor.ru
ORCID iD: 0000-0003-4675-7079

Cand. Sci. (Med.), Chief State Sanitary Doctor for Omsk Oblast, Head

Russian Federation, Omsk

Aleksandra Ya. Nedashkovskaya

Russian Federal Service for Surveillance on Consumer Rights Protection and Human Wellbeing office in Omsk Region

Email: epidotdel@55.rospotrebnadzor.ru
ORCID iD: 0009-0005-3272-3523

Head, Epidemiological surveillance department

Russian Federation, Omsk

Elena Yu. Malinnikova

Mechnikov Research Institute of Vaccines and Sera; Russian Medical Academy of Continuing Professional Education

Email: malinacgb@mail.ru
ORCID iD: 0000-0002-5501-5707

Dr. Sci. (Med.), Associate Professor, Head, Department of virology, Russian Medical Academy of Continuous Professional Education; leading researcher, Laboratory of viral hepatitis, I. Mechnikov Research Institute of Vaccines and Sera

Russian Federation, Moscow; Moscow

Karen K. Kyuregyan

Central Research Institute of Epidemiology; Mechnikov Research Institute of Vaccines and Sera; Russian Medical Academy of Continuing Professional Education

Email: karen-kyuregyan@yandex.ru
ORCID iD: 0000-0002-3599-117X

Dr. Sci. (Biol.), Professor of the RAS, Head, Laboratory of molecular epidemiology of viral hepatitis, Central Research Institute of Epidemiology; leading researcher, Laboratory of viral hepatitis, I. Mechnikov Research Institute of Vaccines and Sera; Professor, Department of virology, Russian Medical Academy of Continuous Professional Education

Russian Federation, Moscow; Moscow; Moscow

Mikhail I. Mikhailov

Central Research Institute of Epidemiology; Mechnikov Research Institute of Vaccines and Sera

Email: michmich2@yandex.ru
ORCID iD: 0000-0002-6636-6801

Dr. Sci. (Med.), Academician of the RAS, chief researcher, Laboratory of molecular epidemiology of viral hepatitis, Central Research Institute of Epidemiology; Head, Laboratory of viral hepatitis, I. Mechnikov Research Institute of Vaccines and Sera

Russian Federation, Moscow; Moscow

Vasily G. Akimkin

Central Research Institute of Epidemiology

Email: crie@pcr.ru
ORCID iD: 0000-0003-4228-9044

Dr. Sci. (Med.), Professor, Academician of the RAS, Director

Russian Federation, Moscow

References

Supplementary files